Drive envelope determination

ABSTRACT

Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.

BACKGROUND

Planning systems utilize information associated with agents in anenvironment to determine actions relative to agents. For example, someexisting planning systems consider distances and actions of agents todetermine a drive path through an environment. However, conventionalmodels have resulted in discrete consideration of individual agents,often making travel more segmented. For example, conventional systemsmay not consider multiple agents when determining an action or reactionwhile travelling in an environment, thereby leading to less than optimalefficiency and/or passenger comfort and satisfaction.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 is a schematic diagram illustrating an example implementation ofgenerating a drive envelope in an environment as described herein.

FIG. 2 is a block diagram illustrating an example computing system forgenerating a drive envelope as described herein.

FIG. 3 is a flowchart illustrating an example method for navigating avehicle through an environment, as described herein.

FIG. 4 includes textual and visual flowcharts to illustrate an examplemethod for generating a drive envelope in an environment

DETAILED DESCRIPTION

Techniques described herein are directed to determining a drive envelopein which a vehicle can travel in an environment. For example, inimplementations described herein, the drive envelope may be constructedby considering a plurality of different agents (e.g., dynamic obstaclesin an environment, including but not limited to pedestrians, animals,cyclists, trucks, motorcycles, other vehicles, or the like, staticobstacles in the environment, including but not limited to road signs,curbs, road markings, physical lane boundaries, or the like, and/orother obstacles, which may be known or unknown) in an environment of avehicle and/or the predicted actions of those agents. In at least oneexample, the drive envelope is a virtual representation in theenvironment that generally defines the constraints and/or boundarieswithin which a vehicle can safely travel relative to the agents toeffectively reach an intended destination. In some examples, the driveenvelope is determined by computing device(s) on an autonomous vehicle,and can be utilized by those computing device(s) to navigate theautonomous vehicle in the environment. That is, the computing device(s)onboard the autonomous vehicle can determine trajectories and/or drivepaths based on the extents of the drive envelope.

As a non-limiting example, a computing device of an autonomous vehiclemay receive sensor data and determine one or more objects in anenvironment and/or attributes of the one or more objects in theenvironment. In some instances, the autonomous vehicle can utilize theobject(s) and/or the attributes of the object(s) to determine whichobject(s) should be included in a drive envelope, to determine extentsof the drive envelope, and/or to determine a trajectory to navigate theautonomous vehicle in the drive envelope.

In some implementations, the drive envelope may be determined by fusingor including data about a number of agents in the environment. Forexample, a predicted path that navigates through the environmentrelative to agents may be determined. At discrete points (or linesegments at discrete points) along the path, lateral distances from thepath to the agents may then be determined. For example, ray castingtechniques may be used to determine the lateral distances at poses of avehicle along the planned path. Based on these distances and informationabout the agent(s), adjusted distances may then be determined. Forexample, semantic information about the agents may be used to classifythe agents and determine a safety factor or safety level associatedtherewith. In some instances, a minimum offset distance of the vehiclefrom an agent, or a safety margin, may be associated with each safetylevel. Probabilistic information about the agents also may be used todetermine the safety level and/or the offset distance of the vehiclerelative to the agent. For instance, probabilistic filtering may beapplied to the distance determinations, e.g., to account for uncertaintyassociated with the lateral distances. For example, uncertainty may beassociated with the measurements, e.g., with calibration and/ortolerances of sensors and/or sensor components, with motion of thesensed agents, e.g., uncertainty associated with potential futuremovement of agents or prediction models that characterize the movement,or the like.

In some examples, once the drive envelope is established, the computingdevice of the autonomous vehicle may determine one or more trajectoriesfor proceeding within the drive envelope. Thus, for example, the driveenvelope may define boundaries of a region in which the vehicle maytravel, and the trajectories may be discrete segments within the driveenvelope according to which the vehicle will travel. Thus, for example,while the drive envelope may be calculated using a planned or predictedpath through the environment, the trajectories may be discrete, shortersegments intended to be carried out by the vehicle to traverse throughthe environment, within the drive envelope.

Techniques described herein are directed to leveraging sensor andperception data to enable a vehicle, such as an autonomous vehicle, tonavigate through an environment while circumventing agents in theenvironment. Techniques described herein can utilize semantic and/orprobabilistic information to determine a drive envelope within which avehicle can travel relative to those agents in a more efficient mannerthan with existing navigation techniques. For example, techniquesdescribed herein may be faster than conventional techniques, as they mayencode large amounts of complex environmental data into a singlerepresentation for efficient optimization. That is, techniques describedherein provide a technological improvement over existing prediction andnavigation technology. In addition to improving the accuracy with whichsensor data can be used to determine the drive envelope, techniquesdescribed herein can provide a smoother ride and improve safety outcomesby, for example, more accurately determining a safe region in which thevehicle may operate to reach an intended destination.

FIGS. 1-4 provide additional details associated with techniquesdescribed herein.

FIG. 1 is a schematic diagram illustrating an example implementation ofdetermining a drive envelope for travelling through an environment asdescribed herein. More specifically, FIG. 1 illustrates an exampleenvironment 100 in which a vehicle 102 is positioned. In the illustratedexample, the vehicle 102 is driving in the environment 100, although inother examples the vehicle 102 may be stationary and/or parked in theenvironment 100. One or more objects, or agents, also are in theenvironment 100. For instance, FIG. 1 illustrates additional vehicles104 a, 104 b and pedestrians 106 a, 106 b in the environment 100. Ofcourse, any number and/or type of agents can additionally oralternatively be present in the environment 100.

For the purpose of illustration, the vehicle 102 can be an autonomousvehicle configured to operate according to a Level 5 classificationissued by the U.S. National Highway Traffic Safety Administration, whichdescribes a vehicle capable of performing all safety-critical functionsfor the entire trip, with the driver (or occupant) not being expected tocontrol the vehicle at any time. In such an example, since the vehicle102 can be configured to control all functions from start to stop,including all parking functions, it can be unoccupied. This is merely anexample, and the systems and methods described herein can beincorporated into any ground-borne, airborne, or waterborne vehicle,including those ranging from vehicles that need to be manuallycontrolled by a driver at all times, to those that are partially orfully autonomously controlled. Additional details associated with thevehicle 102 are described below.

In the example of FIG. 1, the vehicle 102 can be associated with one ormore sensor systems 108. The sensor system(s) 108 can generate sensordata 110, which can be utilized by vehicle computing device(s) 112associated with the vehicle 102 to recognize the one or more agents,e.g., the vehicles 104 and the pedestrians 106. The sensor system(s) 108can include, but is/are not limited to, light detection and ranging(LIDAR) sensors, radio detection and ranging (RADAR) sensors, ultrasonictransducers, sound navigation and ranging (SONAR) sensors, locationsensors (e.g., global positioning system (GPS), compass, etc.), inertialsensors (e.g., inertial measurement units, accelerometers,magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity,depth, etc.), wheel encoders, microphones, environment sensors (e.g.,temperature sensors, humidity sensors, light sensors, pressure sensors,etc.), etc.

In at least one example, the vehicle computing device(s) 112 can includea perception system, which can perform agent detection, segmentation,and/or classification based at least in part on the sensor data 110received from the sensor system(s) 108. For instance, the perceptionsystem can detect the vehicles 104 a, 104 b and/or the pedestrians 106a, 106 b in the environment 100 based on the sensor data 110 generatedby the sensor system(s) 108. Additionally, the perception system candetermine an extent of the vehicles 104 a, 104 b and/or the pedestrians106 a, 106 b (e.g., height, weight, length, etc.), a pose of thevehicles 104 a, 104 b and/or the pedestrians 106 a, 106 b (e.g.,x-coordinate, y-coordinate, z-coordinate, pitch, roll, yaw), etc. Thesensor system(s) 108 can continuously generate the sensor data 110(e.g., in near-real time), which can be utilized by the perceptionsystem (and other systems of the vehicle computing device(s) 112).

The vehicle computing device(s) 112 can also include a drive envelopedetermination system 114, which, as described further herein, candetermine a drive envelope 116. The drive envelope 116 may be a virtualarea in the environment 100 in which the vehicle 102 can travel safelythrough the environment 100, e.g., relative to agents including thevehicles 104 a, 104 b and the pedestrians 106 a, 106 b. As illustratedin FIG. 1 and detailed further herein, the drive envelope 116 may bedefined by virtual boundaries 118. In the example drive envelope 116illustrated in FIG. 1, the drive envelope 116 may have a variableenvelope width 120, e.g., in the width or lateral direction of thevehicle 102. (FIG. 1 illustrates a first envelope width 120 a and asecond envelope width 120 b, which collectively, and along with otherwidths at other positions in the drive envelope 116, may be referred toas the envelope width 120.) As described herein, attributes of thevirtual boundaries 118, including the envelope width 120, may bedetermined based at least in part on the sensor data 110 and/ordeterminations made by the perception system. For example, in someimplementations, the virtual boundaries 118 may be determined based onagent information 122, which may include information about semanticclassifications and/or probabilistic models. In at least some examples,virtual boundaries 118 may be encoded with information (such as lateraldistance to the nearest agent, semantic classification of the nearestagent, etc.).

As also illustrated in FIG. 1, upon determining the drive envelope 118,the vehicle computing device(s) 112 also may determine a travel path 124along which the vehicle 102 may travel through the environment 100. Theplanner system can determine routes and/or trajectories to use tocontrol the vehicle 102 based at least in part on the sensor data 110received from the sensor system(s) 108 and/or any determinations made bythe perception system. For instance, the planning system can determineroutes to navigate safely through the environment relative to thevehicle 104 a, 104 b and the pedestrians 106 a, 106 b.

More specifically, FIG. 1 illustrates a scenario in which the vehicle102 is travelling through the environment 100, generally in thedirection of arrow 126. The vehicle 102 is travelling on a road 128having a first lane 130 and a second lane 132. The vehicle 102 is in thesecond lane 130, behind the vehicle 104 a, and, in the example, istravelling relatively faster than the vehicle 104 a. For example, thevehicle 104 a may be slowing down to turn left, to parallel park on theleft side of the road 126, to drop off a passenger or delivery on theleft shoulder of the road, or the like. In examples of this disclosure,the vehicle 102, e.g., by executing the drive envelope determinationsystem 114, may determine the drive envelope 116 as a precursor todetermining whether the vehicle 102 can safely navigate around theslowing vehicle 104 a. More specifically, the vehicle computingdevice(s) 112, using the sensor data 110 and/or the object information122, may determine information about agents in the environment 100,which may include the vehicle 104 a, the vehicle 104 b, the pedestrian106 a, the pedestrian 106 b, and/or additional agents. For example, andas will be described further herein, the vehicle computing device(s)112, using the drive envelope determination system 114, may fuse orcombine information about each of the agents, including the objectinformation 122, to configure the drive envelope 116. Such a driveenvelope 116 may, in turn, be used to determine one or more trajectories(e.g. by providing constrains and/or boundaries) along which vehicle 102may travel.

With further reference to FIG. 1, the drive envelope 116 may havevarying widths, e.g., the first width 120 a and a second width 120 b,and those widths are determined at least in part based on the objectinformation 122. For example, at a position immediately in front of thevehicle 102 in the direction illustrated by arrow 126, the width 120 agenerally spans the entire width of the road 126. At edges of the firstlane 128 and the second lane 130, a slight offset relative to theshoulder may be provided, although such an offset may be excluded inother embodiments. Further along the road 126 in the direction of thearrow 124, the drive envelope 116 begins to narrow as the boundary 118gradually moves away from the left shoulder until the drive envelope 116is completely confined in the right lane 130. This narrowing of thedrive envelope 116 is a result of the presence and/or the relativedeceleration of the vehicle 104 a. Moreover, immediately adjacent thevehicle 104 a, the drive envelope 116 may further narrow, e.g., toprovide a minimum lateral offset relative to the vehicle 104 a.Similarly, on the right side of the drive envelope 116, offsets may beprovided proximate the first pedestrian 106 a and the vehicle 104 b,which is parked on the right shoulder of the road 126. In some examples,such a drive envelope 116 may also be determined based on a predictedtrajectory of agents, such as that of pedestrian 106 a. As anon-limiting example depicted in FIG. 1, the drive envelope 116 indentsslightly near the pedestrian 106 a in anticipation of the pedestrian 106a continuing walking in the direction of travel 126. The length of suchan indent may be based on, for example, the velocities of the vehicle102 and the pedestrian 106 a.

As described further herein, the width 120 of the drive envelope 116varies as a result of information about agents in the environment. Thus,for example, offsets are provided proximate the vehicle 104 a, thevehicle 104 b, and the pedestrian 106 a. These offsets may serve toprovide a safety margin or minimum distance of the vehicle 102 from theobjects, as the vehicle 102 travels in the drive envelope 116. In someimplementations, the offsets and/or the minimum distances may differamong and between agents in the environment 100. For example, semanticinformation about the agents may be used to determine appropriateoffsets. Thus, in FIG. 1, pedestrians may be associated with a firstclassification and vehicles may be associated with a secondclassification. The first classification may correspond to a minimumoffset larger than a minimum offset to which the second classificationcorresponds. Moreover, vehicles within the second classification may befurther classified as static, as in the case of the vehicle 104 b ordynamic, as in the case of the vehicle 104 a. An offset relative to themoving vehicle 104 a may be greater than the offset associated with theparked vehicle 104 b, for example. In further examples, probabilisticinformation about the agents may also or alternatively be used todetermine the offsets. For example, there may be some uncertaintyassociated with the actions of the moving pedestrian 106 a and/or themoving vehicle 104 a, e.g., because the pedestrian 106 a or the driverof the vehicle 104 a may change course. Probabilistic information mayalso be used to account for sensor inaccuracies, e.g., associated withthe sensor system(s) 108.

The drive envelope 116 may allow for more efficient navigation aroundthe vehicle 104 a when compared with conventional navigation techniques.In conventional solutions, for example, the vehicle 102 may slow down asit approaches the vehicle 104 a, e.g., to maintain a minimum distancebetween the vehicle 102 and the vehicle 104 a, and only upon stopping orslowing to a threshold speed may the vehicle 102 begin to seek out analternative route around the vehicle 104 a. Alternatively, the vehicle102 may idle in the lane 130 until the vehicle 104 a turns, parks,moves, accelerates, or the like. However, in implementations of thisdisclosure, the drive envelope 116 considers multiple agents, as well asthe object information 122, to provide a more robust understanding ofthe environment 100. This understanding may enhance decision makingand/or allow for more effective and efficient control of the vehicle 102as it travels through the environment 100. With specific regard to FIG.1, such enhanced decision-making may allow the vehicle to traverse alongthe drive path 124, without the need to slow down behind the vehicle 104a and/or await additional decision making. Further, the drive envelope116 provides boundaries for a wide range of possible trajectories whichmay be employed to safely navigate a portion of a path. By providingsuch a wide range of safe trajectories, the vehicle 102 is able toquickly overcome any hazards, while incorporating information about allagents in a scene.

FIG. 1 illustrates a single example of using a drive envelope tonavigate in an environment. Myriad other examples also are contemplated.For example, the drive envelope may be used in any environment to betternavigate agents.

FIG. 2 is a block diagram illustrating an example system 200 forgenerating and utilizing a drive envelope as described herein. In atleast one example, the system 200 can include a vehicle 202, which canbe the same vehicle as the vehicle 102 described above with reference toFIG. 1. The vehicle 202 can include one or more vehicle computingdevices 204, one or more sensor systems 206, one or more emitters 208,one or more communication connections 210, at least one directconnection 212, and one or more drive modules 214. In at least oneexample, the sensor system(s) 206 can correspond to the sensor system(s)108 described above with reference to FIG. 1. Further, in at least oneexample, the vehicle computing device(s) 204 can correspond to thevehicle computing device(s) 112 described above with reference to FIG.1.

The vehicle computing device(s) 204 can include processor(s) 216 andmemory 218 communicatively coupled with the processor(s) 216. In theillustrated example, the vehicle 202 is an autonomous vehicle; however,the vehicle 202 could be any other type of vehicle. In the illustratedexample, the memory 218 of the vehicle computing device(s) 204 stores alocalization system 220, a perception system 222, a prediction system224, a planning system 226, a drive envelope determination system 228,and one or more system controllers 230. Although these systems andcomponents are illustrated, and will be described below, as separatecomponents for ease of understanding, functionality of the varioussystems and controllers may be attributed differently than discussed. Byway of non-limiting example, functionality attributed to the perceptionsystem 224 may be carried out by the localization system 220 and/or theprediction system 224. Moreover, fewer or more systems and componentsmay be utilized to perform the various functionalities described herein.Furthermore, though depicted in FIG. 2 as residing in memory 218 forillustrative purposes, it is contemplated that the localization system220, the perception component 222, the prediction system 224, theplanning system 226, the drive envelope determination component 228,and/or the one or more system controllers 230 can additionally, oralternatively, be accessible to the vehicle 202 (e.g., stored on, orotherwise accessible by, memory remote from the vehicle 202).

As also illustrated in FIG. 2, the memory 218 can include a map storage232, which may store one or more maps, and/or agent information storage232, which may store the agent information 122. A map can be any numberof data structures modeled in two dimensions or three dimensions thatare capable of providing information about an environment, such as, butnot limited to, topologies (such as intersections), streets, mountainranges, roads, terrain, and the environment in general. As discussedfurther herein, the agent information 122 may be any information aboutagents, including, without limitation, information that is useful forinterpreting sensor data to classify or otherwise understand agents inthe environment.

In at least one example, the localization system 220 can includefunctionality to receive data from the sensor system(s) 206 to determinea position and/or orientation of the vehicle 202 (e.g., one or more ofan x-, y-, z-position, roll, pitch, or yaw). For example, thelocalization system 220 can include and/or request/receive a map of anenvironment (e.g., from the map storage 232) and can continuouslydetermine a location and/or orientation of the autonomous vehicle withinthe map. In some instances, the localization system 220 can utilize SLAM(simultaneous localization and mapping), CLAMS (calibration,localization and mapping, simultaneously), relative SLAM, bundleadjustment, non-linear least squares optimization, differential dynamicprogramming, or the like to receive image data, LIDAR data, radar data,IMU data, GPS data, wheel encoder data, and the like to accuratelydetermine a location of the autonomous vehicle. In some instances, thelocalization system 220 can provide data to various components of thevehicle 202 to determine an initial position of an autonomous vehiclefor generating a candidate trajectory for travelling in the environment.

In some instances, the perception system 222 can include functionalityto perform object detection, segmentation, and/or classification. Insome examples, the perception system 222 can provide processed sensordata that indicates a presence of an agent that is proximate to thevehicle 202, such as the vehicles 104 a, 104 b and/or the pedestrians106 a, 106 b. The perception system may also include a classification ofthe entity as an entity type (e.g., car, pedestrian, cyclist, animal,building, tree, road surface, curb, sidewalk, unknown, etc.). Forinstance, the perception system 222 may compare sensor data to the agentinformation 120 in the agent information database 234 to determine theclassification. In additional and/or alternative examples, theperception system 222 can provide processed sensor data that indicatesone or more characteristics associated with a detected agent and/or theenvironment in which the agent is positioned. In some examples,characteristics associated with an agent can include, but are notlimited to, an x-position (global and/or local position), a y-position(global and/or local position), a z-position (global and/or localposition), an orientation (e.g., a roll, pitch, yaw), an agent type(e.g., a classification), a velocity of the agent, an acceleration ofthe agent, an extent of the agent (size), etc. Characteristicsassociated with the environment can include, but are not limited to, apresence of another agent in the environment, a state of another agentin the environment, a time of day, a day of a week, a season, a weathercondition, an indication of darkness/light, etc.

The prediction system 224 can access sensor data from the sensorsystem(s) 206, map data from the map storage 232, and, in some examples,perception data output from the perception system 222 (e.g., processedsensor data). In at least one example, the prediction system 224 candetermine features associated with the agent based at least in part onthe sensor data, the map data, and/or the perception data. As describedabove, features can include an extent of an agent (e.g., height, weight,length, etc.), a pose of an agent (e.g., x-coordinate, y-coordinate,z-coordinate, pitch, roll, yaw), a velocity of an agent, an accelerationof an agent, and a direction of travel of an agent (e.g., a heading).Moreover, the prediction system 224 may be configured to determine adistance between an agent and a proximate driving lane, a width of acurrent driving lane, proximity to a crosswalk, semantic feature(s),interactive feature(s), etc.

The prediction system 224 can analyze features of agents to predictfuture actions of the agents. For instance, the prediction system 224can predict lane changes, decelerations, accelerations, turns, changesof direction, or the like. The prediction system 224 can send predictiondata to the drive envelope determination system 228 so that the driveenvelope determination system 228 can utilize the prediction data todetermine the boundaries of the drive envelope (e.g. based on one ormore of an uncertainty in position, velocity, acceleration in additionto, or alternatively, with a semantic classification of the agent). Forinstance, if the prediction data indicates that a pedestrian walkingalong the shoulder is behaving erratically, the drive envelopedetermination system 228 can determine an increased offset of the driveenvelope proximate the pedestrian. In some examples where the vehicle202 is not autonomous, the prediction system 224 may provide anindication (e.g., an audio and/or visual alert) to a driver of apredicted event that may affect travel.

In general, the planning system 226 can determine a path for the vehicle202 to follow to traverse through an environment. For example, theplanning system 226 can determine various routes and trajectories andvarious levels of detail. For example, the planning system 226 candetermine a route to travel from a first location (e.g., a currentlocation) to a second location (e.g., a target location). For thepurpose of this discussion, a route can be a sequence of waypoints fortravelling between two locations. As non-limiting examples, waypointsinclude streets, intersections, global positioning system (GPS)coordinates, etc. Further, the planning system 226 can generate aninstruction for guiding the autonomous vehicle along at least a portionof the route from the first location to the second location. In at leastone example, the planning system 226 can determine how to guide theautonomous vehicle from a first waypoint in the sequence of waypoints toa second waypoint in the sequence of waypoints. In some examples, theinstruction can be a trajectory, or a portion of a trajectory. In someexamples, multiple trajectories can be substantially simultaneouslygenerated (e.g., within technical tolerances) in accordance with areceding horizon technique, wherein one of the multiple trajectories isselected for the vehicle 202 to navigate. Thus, in exampleimplementations described herein, the planning system 226 may generatetrajectories along which the vehicle can navigate, with the trajectoriesbeing contained within the drive envelope.

The drive envelope determination system 228 is configured to determine adrive envelope, such as the drive envelope 116. Although illustrated asa separate block in the memory 218, in some examples andimplementations, the drive envelope determination system 228 may be apart of the planning system 226. The drive envelope determination system228 can access sensor data from the sensor system(s) 206, map data fromthe map storage 232, agent information from the agent information store234, outputs from one or more of the localization system 220, theperception system 222, and/or the prediction system 224 (e.g., processeddata). By way of non-limiting example, the drive envelope determinationsystem 228 may access (e.g., retrieve or receive) one or more plannedpaths. The planned paths may represent potential paths to navigate theenvironment, and may be determined based on map data, agent information,and/or perception data, for example. In some examples, the planned pathsmay be determined as candidate paths for carrying out a mission. Forinstance, the vehicle computing device(s) 204, may define or determine amission as a highest-level of navigation to a destination, e.g., aseries of roads for navigating to the destination. Once the mission isdetermined, one or more actions for carrying out that high-levelnavigation may then be determined. Actions may include more frequentdeterminations of how to carry out the mission. For example, actions mayinclude tasks such as “follow vehicle,” “pass vehicle on the right,” orthe like. In some examples, the projected paths described herein may bedetermined for each action.

For the planned path(s), the drive envelope determination system 228 maydetermine, at discrete points along the planned path(s), lateraldistances from the path to agents in the environment. For example, thedistances may be received as perception data generated by the perceptionsystem 222, and/or may be determined using mathematical and/or computervision models, such as ray casting techniques. Various lateral distancesmay then be adjusted to account for other factors. For example, it maybe desirable to maintain a minimum distance between the vehicle 202 andagents in the environment. In implementations of this disclosure,information about the agents, including semantic classifications, may beused to determine those distance adjustments. Moreover, the predictionsystem 224 may also provide prediction data about a predicted movementof the agents, and the distances may further be adjusted based on thosepredictions. For example, the prediction data may include a confidencescore and the lateral distance may be adjusted based on the confidencescore, e.g., by making a greater adjustment for less confidentpredictions and slighter or no adjustments for more confidentpredictions. Using the adjusted distances, the drive envelopedetermination system 228 may define boundaries of the drive envelope. Inat least some examples, the boundaries may be discretized (e.g. every 10cm, 50 cm, 1 m, etc) and information regarding the boundary may beencoded (e.g. lateral distance to the nearest agent, semanticclassification of the nearest agent, confidence and/or probability scoreassociated with the boundary, etc.). As described herein, the trajectorydetermined by the planning system 224 may be confined by, and inaccordance with, in the drive envelope. While the drive envelopedetermination system 228 is illustrated as being separate from theplanning system 226, one or more of the functionalities of the driveenvelope system 228 may be carried out by the planning system 226. Insome embodiments, the drive envelope determination system 228 may be apart of the planning system 226.

In at least one example, the localization system 220, the perceptionsystem 222, the prediction system 224, the planning system 226, and/orthe drive envelope determination system 228 can process sensor data, asdescribed above, and can send their respective outputs over network(s)236, to computing device(s) 238. In at least one example, thelocalization system 220, the perception system 222, the predictionsystem 224, and/or the planning system 226 can send their respectiveoutputs to the computing device(s) 238 at a particular frequency, aftera lapse of a predetermined period of time, in near real-time, etc.

In at least one example, the vehicle computing device(s) 204 can includeone or more system controllers 230, which can be configured to controlsteering, propulsion, braking, safety, emitters, communication, andother systems of the vehicle 202. These system controller(s) 230 cancommunicate with and/or control corresponding systems of the drivemodule(s) 214 and/or other components of the vehicle 202. For example,the system controllers 230 may cause the vehicle to traverse along adrive path determined by the planning system 226, e.g., in the driveenvelope 116 determined by the drive envelope determination system 228.

In at least one example, the sensor system(s) 206 can include LIDARsensors, RADAR sensors, ultrasonic transducers, SONAR sensors, locationsensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertialmeasurement units, accelerometers, magnetometers, gyroscopes, etc.),cameras (e.g., RGB, UV, IR, intensity, depth, etc.), microphones, wheelencoders, environment sensors (e.g., temperature sensors, humiditysensors, light sensors, pressure sensors, etc.), etc. The sensorsystem(s) 306 can include multiple instances of each of these or othertypes of sensors. For instance, the LIDAR sensors can include individualLIDAR sensors located at the corners, front, back, sides, and/or top ofthe vehicle 202. As another example, the camera sensors can includemultiple cameras disposed at various locations about the exterior and/orinterior of the vehicle 202. The sensor system(s) 206 can provide inputto the vehicle computing device(s) 204. Additionally and/oralternatively, the sensor system(s) 306 can send sensor data, via thenetwork(s) 236, to the computing device(s) 238 at a particularfrequency, after a lapse of a predetermined period of time, in nearreal-time, etc.

The vehicle 202 can also include one or more emitters 208 for emittinglight and/or sound. The emitter(s) 208 in this example include interioraudio and visual emitters to communicate with passengers of the vehicle202. By way of example and not limitation, interior emitters can includespeakers, lights, signs, display screens, touch screens, haptic emitters(e.g., vibration and/or force feedback), mechanical actuators (e.g.,seatbelt tensioners, seat positioners, headrest positioners, etc.), andthe like. The emitter(s) 208 in this example also include exterioremitters. By way of example and not limitation, the exterior emitters inthis example include light emitters (e.g., indicator lights, signs,light arrays, etc.) to visually communicate with pedestrians, otherdrivers, other nearby vehicles, etc., one or more audio emitters (e.g.,speakers, speaker arrays, horns, etc.) to audibly communicate withpedestrians, other drivers, other nearby vehicles, etc., etc. In atleast one example, the emitter(s) 208 can be disposed at variouslocations about the exterior and/or interior of the vehicle 202.

The vehicle 202 can also include communication connection(s) 210 thatenable communication between the vehicle 202 and other local or remotecomputing device(s). For instance, the communication connection(s) 210can facilitate communication with other local computing device(s) on thevehicle 202 and/or the drive module(s) 214. Also, the communicationconnection(s) 310 can allow the vehicle to communicate with other nearbycomputing device(s) (e.g., other nearby vehicles, traffic signals,etc.). The communications connection(s) 210 also enable the vehicle 202to communicate with a remote teleoperations computing device or otherremote services.

The communications connection(s) 210 can include physical and/or logicalinterfaces for connecting the vehicle computing device(s) 204 to anothercomputing device or a network, such as network(s) 232. For example, thecommunications connection(s) 210 can enable Wi-Fi-based communicationsuch as via frequencies defined by the IEEE 802.11 standards, shortrange wireless frequencies such as BLUETOOTH®, or any suitable wired orwireless communications protocol that enables the respective computingdevice to interface with the other computing device(s).

In at least one example, the vehicle 202 can include drive module(s)214. In some examples, the vehicle 202 can have a single drive module214. In at least one example, if the vehicle 202 has multiple drivemodules 214, individual drive modules 214 can be positioned on oppositeends of the vehicle 202 (e.g., the front and the rear, etc.). In atleast one example, the drive module(s) 214 can include sensor system(s)to detect conditions of the drive module(s) 214 and/or the surroundingsof the vehicle 202. By way of example and not limitation, the sensorsystem(s) 206 can include wheel encoder(s) (e.g., rotary encoders) tosense rotation of the wheels of the drive module, inertial sensors(e.g., inertial measurement units, accelerometers, gyroscopes,magnetometers, etc.) to measure position and acceleration of the drivemodule, cameras or other image sensors, ultrasonic sensors toacoustically detect objects in the surroundings of the drive module,LIDAR sensors, RADAR sensors, etc. Some sensors, such as the wheelencoder(s) can be unique to the drive module(s) 214. In some cases, thesensor system(s) on the drive module(s) 214 can overlap or supplementcorresponding systems of the vehicle 202 (e.g., the sensor system(s)206).

The drive module(s) 214 can include many of the vehicle systems,including a high voltage battery, a motor to propel the vehicle 202, aninverter to convert direct current from the battery into alternatingcurrent for use by other vehicle systems, a steering system including asteering motor and steering rack (which can be electric), a brakingsystem including hydraulic or electric actuators, a suspension systemincluding hydraulic and/or pneumatic components, a stability controlsystem for distributing brake forces to mitigate loss of traction andmaintain control, an HVAC system, lighting (e.g., lighting such ashead/tail lights to illuminate an exterior surrounding of the vehicle),and one or more other systems (e.g., cooling system, safety systems,onboard charging system, other electrical components such as a DC/DCconverter, a high voltage junction, a high voltage cable, chargingsystem, charge port, etc.). Additionally, the drive module(s) 214 caninclude a drive module controller which can receive and preprocess datafrom the sensor system(s) and to control operation of the variousvehicle systems. In some examples, the drive module controller caninclude processor(s) and memory communicatively coupled with theprocessor(s). The memory can store one or more modules to performvarious functionalities of the drive module(s) 214. Furthermore, thedrive module(s) 214 also include communication connection(s) that enablecommunication by the respective drive module with other local or remotecomputing device(s).

As described above, the vehicle 202 can send sensor data to thecomputing device(s) 238, via the network(s) 236. In some examples, thevehicle 202 can send raw sensor data to the computing device(s) 236. Inother examples, the vehicle 202 can send processed sensor data and/orrepresentations of sensor data to the computing device(s) 238 (e.g.,data output from the localization system 220, the perception system 222,the prediction system 224, and/or the planning system 226). In someexamples, the vehicle 202 can send sensor data to the computingdevice(s) 238 at a particular frequency, after a lapse of apredetermined period of time, in near real-time, etc.

The computing device(s) 238 can receive the sensor data (raw orprocessed) from the vehicle 202 and/or one or more other vehicles and/ordata collection devices, and can determine a drive envelope based on thesensor data and other information. In at least one example, thecomputing device(s) 238 can include processor(s) 240 and memory 242communicatively coupled with the processor(s) 240. In the illustratedexample, the memory 242 of the computing device(s) 238 stores a driveenvelope determination component 244 which can include objectinformation storage 246, for example. In at least one example, the agentinformation storage 246 can correspond to the agent information storage230 and/or the agent information storage 122.

The drive envelope determination component 244 may correspond to thedrive envelope determination system 228 described above. For example,the drive envelope determination component 244 may process data todetermine a drive envelope remote from the vehicle. For example, thedrive envelope (or a preferred drive envelope from a plurality of driveenvelopes) may be determined at the computing device(s) 238 andtransferred back to the vehicle 202, e.g., via the networks 236.Moreover, the drive envelope determination component 244 can perform oneor more operations as described above and ascribed to the localizationsystem 220, the perception system 222, the prediction system 224, and/orthe planning system 226. In at least one example, the drive envelopedetermination component 244 can analyze the sensor data to determineattributes of agents in the environment to (1) determine whether suchagent(s) should be included in a drive envelope and (2) configureboundaries of the drive envelope. For example, the drive envelopedetermination component 244 may compare information about agents toclassification information, e.g., stored in the agent informationstorage 246, to determine whether the agent will be impacted by thevehicle, whether the agent (or a predicted movement of the agent) willimpact the vehicle, and/or the extent to which the agent should impactthe drive envelope.

The drive envelope determination component 244 may also or alternativelyconsider features of agents to characterized the drive envelope.Features can include an extent of an agent (e.g., height, weight,length, etc.), a pose of an agent (e.g., x-coordinate, y-coordinate,z-coordinate, pitch, roll, yaw), a velocity of an agent, an accelerationof an agent, a direction of travel of an agent (e.g., a heading), adistance between an agent and a proximate driving lane, a semanticclassification of the agent (car, pedestrian, bicyclist, etc.), a widthof a current driving lane, proximity to a crosswalk, semanticfeature(s), interactive feature(s), etc. In some examples, at least someof the features (e.g., the extent, the pose, etc.) can be determined bya perception system onboard a sensor data source (e.g., the vehicle 202and/or the other vehicle(s) and/or data collection device(s)) and otherof the features can be determined based on such features and/or othersensor data and/or map data associated with a map of the environment. Insome examples, a sample can be broken into one or more shorter periodsof time and features can be determined for each of the shorter periodsof time. The features for the sample can be determined based on atotality of features from each of the shorter periods of time.

The processor(s) 216 of the vehicle 202 and the processor(s) 240 of thecomputing device(s) 236 can be any suitable processor capable ofexecuting instructions to process data and perform operations asdescribed herein. By way of example and not limitation, the processor(s)216 and 236 can comprise one or more Central Processing Units (CPUs),Graphics Processing Units (GPUs), or any other device or portion of adevice that processes electronic data to transform that electronic datainto other electronic data that can be stored in registers and/ormemory. In some examples, integrated circuits (e.g., ASICs, etc.), gatearrays (e.g., FPGAs, etc.), and other hardware devices can also beconsidered processors in so far as they are configured to implementencoded instructions.

Memory 218 and 242 are examples of non-transitory computer-readablemedia. Memory 218 and 242 can store an operating system and one or moresoftware applications, instructions, programs, and/or data to implementthe methods described herein and the functions attributed to the varioussystems. In various implementations, the memory can be implemented usingany suitable memory technology, such as static random access memory(SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory,or any other type of memory capable of storing information. Thearchitectures, systems, and individual elements described herein caninclude many other logical, programmatic, and physical components, ofwhich those shown in the accompanying figures are merely examples thatare related to the discussion herein.

It should be noted that while FIG. 2 is illustrated as a distributedsystem, in alternative examples, components of the vehicle 202 can beassociated with the computing device(s) 236 and/or components of thecomputing device(s) 236 can be associated with the vehicle 202. That is,the vehicle 202 can perform one or more of the functions associated withthe computing device(s) 236, and vice versa. Moreover, although varioussystems and components are illustrated as being discrete systems, theillustrations are examples only, and more or fewer discrete systems mayperform the various functions described herein.

FIGS. 3 and 4 are flowcharts showing example methods involving driveenvelope determination as described herein. Specifically, FIG. 3illustrates a method 300 in which objects in the environment are used tocreate a drive envelope and the drive envelope is used to determine atrajectory through the environment, e.g., so the vehicle can travelthrough the environment along the trajectory. FIG. 4 illustrates amethod 400 that describes and illustrates drive envelope determinationin more detail. The methods illustrated in FIGS. 3 and 4 are describedwith reference to the vehicles 102 and/or 202 shown in FIGS. 1 and 2 forconvenience and ease of understanding. However, the methods illustratedin FIGS. 3 and 4 are not limited to being performed using the vehicles102, 202, and can be implemented using any of the other vehiclesdescribed in this application, as well as vehicles other than thosedescribed herein. Moreover, the vehicles 102, 202 described herein arenot limited to performing the methods illustrated in FIGS. 3 and 4.

The methods 300, 400 are illustrated as collections of blocks in logicalflow graphs, which represent sequences of operations that can beimplemented in hardware, software, or a combination thereof. In thecontext of software, the blocks represent computer-executableinstructions stored on one or more computer-readable storage media that,when executed by processor(s), perform the recited operations.Generally, computer-executable instructions include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described blocks can becombined in any order and/or in parallel to implement the processes. Insome embodiments, one or more blocks of the process can be omittedentirely. Moreover, the methods 300, 400 can be combined in whole or inpart with each other or with other methods.

FIG. 3 is a flowchart illustrating an example method 300 for traversingin an environment using a drive envelope, as described herein.

Block 302 illustrates receiving sensor data associated with anenvironment. As described above, a vehicle 202 can include sensorsystem(s) 206. In at least one example, the sensor system(s) 206 caninclude LIDAR sensors, RADAR sensors, ultrasonic transducers, SONARsensors, location sensors (e.g., GPS, compass, etc.), inertial sensors(e.g., inertial measurement units, accelerometers, magnetometers,gyroscopes, etc.), cameras (e.g., RGB, IR, intensity, depth, etc.),microphones, wheel encoders, environment sensors (e.g., temperaturesensors, humidity sensors, light sensors, pressure sensors, etc.), etc.The sensor system(s) 206 can provide input to the vehicle computingdevice(s) 204, and one or more systems of the vehicle computingdevice(s) 204 can utilize the input. For instance, in at least oneexample, the vehicle computing device(s) 204 can receive the input forprocessing by the localization system 220, the perception system 222,the prediction system 224, the planning system 226, and/or the driveenvelope determination system 228.

As described above, in at least one example, the vehicle 202 can sendsensor data to the computing device(s) 238, via the network(s) 236. Insome examples, the vehicle 202 can send raw sensor data to the computingdevice(s) 238. In other examples, the vehicle 202 can send processedsensor data and/or representations of sensor data to the computingdevice(s) 238 (e.g., data output from the localization system 220, theperception system 222, the prediction system 224, and/or the planningsystem 226). In some examples, the vehicle 202 can send sensor data tothe computing device(s) 238 at a particular frequency, after a lapse ofa predetermined period of time, in near real-time, etc. Sensor datacollected over time can correspond to log data.

Block 304 illustrates determining a plurality of objects in theenvironment. For example, as described above, a number of agents may bepresent in an environment in which an autonomous vehicle is travelling.In FIG. 1, the agents may include the vehicles 104 a, 104 b and thepedestrians 106 a, 106 b. Based on the sensor data received at block302, the vehicle computing devices 204 may, e.g., via image processing,vision detection, classification and/or segmentation, LIDAR detection,segmentation, and/or classification, and/or other processes, recognizethe agents. In addition to the example agents discussed herein, block304 may also determine additional details about the environment and/orfeatures in the environment.

Block 306 illustrates determining information about each of the objects.For example, the information about each of the objects may includephysical features associated with the objects, including but not limitedto an extent of an object (e.g., height, weight, length, etc.), a poseof the object (e.g., x-coordinate, y-coordinate, z-coordinate, pitch,roll, yaw), a velocity of the object, acceleration of the object, adirection of travel of the object (e.g., heading), a semanticclassification, etc., as described above. Additionally or alternatively,such features can include an orientation relative to a sensor (e.g.,which direction is a pedestrian facing), distance to a map feature(e.g., a start of a crosswalk, an end of a crosswalk, a side of acrosswalk, a start of a junction, an end of a junction, a side of ajunction, etc.), semantic features (e.g., crosswalk sign, trafficlights, etc.), feasibility of performing an intent (e.g., cross-abilityof a crosswalk, etc.), etc. In some examples, data from one or more ofthe localization system, the planner system, the prediction system,and/or the perception system may be used at block 306 to determine thefeatures. The features may also include a predicted trajectory of theagent(s) and/or course of action. In at least one example, the vehiclemay analyze the sensor data and/or the map data to determine an eventassociated with an agent (e.g., a cut-in event, a crosswalk event,etc.).

Block 308 illustrates determining a drive envelope. For example, asdescribed herein, the drive envelope may represent a virtual portion ofthe environment in which the vehicle may travel relative to objects.FIG. 4 illustrates example actions for determining a drive envelope, andwill be described in more detail, below.

Blocks 310, 212 and 314 may also be illustrative of actions carried outto determine the drive envelope at block 308. More specifically, block310 illustrates determining whether to include each of the objects in adrive envelope. For example, based on the information of the objects,the vehicle may determine that certain objects are likely to have noimpact on the vehicle, and thus may be excluded from the drive envelope,at block 310. For example, agents may be excluded based on theirposition relative to the vehicle 202, based on their velocity relativeto that of the vehicle. In the specific example of FIG. 1, block 308 maydetermine that the pedestrian 106 b should be excluded fromdetermination of a drive envelope because of the pedestrian's 106 bposition and/or speed relative to the vehicle 102 b. In additionalexamples, agents in front of the vehicle in the direction of travel maybe excluded from consideration, for example, if it is determined thatthey are beyond a threshold distance, and/or have a relatively highervelocity.

In other examples, a predicted motion of the agents may be used todetermine whether the agents are to be included or excluded. By way ofnon-limiting example, when it is determined that an agent in an adjacentlane is likely cut in front of the vehicle, that agent may be consideredwith additional processing, e.g., because its action could impact and/orpotentially interfere with operation of the vehicle. In such exampleimplementations, it may be desirable to consider the agent individually,including using higher order computations and processing. Similarly,maneuvers of the controlled vehicle may be considered when determiningwhether to include agents in the drive envelope determination. Forexample, when it is determined that a certain maneuver will require anaction by an agent, e.g., the agent may need to change course or speedto allow the vehicle to maneuver, the agent may be excluded from thedrive envelope determination. One example of such a maneuver may a lanechange in which the agent may have to decelerate to make room for thevehicle. Because the maneuver requires some action on the part of theagent, the vehicle computing devices may monitor the agent more closely,rather than consider the agent solely via the drive envelope.

If it is determined at block 310 that an agent is not to be included,block 312 illustrates that such agents are excluded. Alternatively,block 314 illustrates determining a drive envelope using information ofeach agent determined to be included at block 310. As described herein,the drive envelope may be determined as a virtual space in theenvironment in which the vehicle can travel. The drive envelope mayinclude boundaries that provide offsets from the agents, which may bedetermined based on semantic properties of the agent(s), probabilisticmodels (including models of the motion of agents, sensor measurements,and the like), or other information. In at least some examples, discreteportions (e.g. every 10 cm, 50 cm, 1 m, etc.) along the drive envelopeboundaries may be encoded with additional information, such as, but notlimited to, distance to the nearest agent, semantic classification ofthe nearest agent, uncertainties, confidences, etc. Moreover, in someimplementations, more than one drive envelope may be determined. Forexample, much like real world drivers may have multiple options foravoiding an agent, e.g., pass on the right or pass on the left on athree-lane road, multiple drive envelopes may be determined for morethan one potential planned path. In these examples, one drive envelopeis selected for implementation. For example, a drive envelope may bepreferred because it has a larger area, because it better conforms totraffic laws (e.g., pass a vehicle on the left instead of the right),because it avoids the most agents, or for one or more additionalreasons. An example of determining a drive envelope is detailed furtherbelow in connection with FIG. 4.

As will be appreciated, as the vehicle moves through the environment,agents in the environment are ever-changing. Accordingly, the method 300(and the method 400) may be carried out iteratively. For example, newdrive envelopes may be determined in near real-time, e.g., at intervalsof from about 0.5 seconds to about 3 seconds. Moreover, as agents changepositions, velocities, attitudes, and the like, relative to the vehicle,agents previously excluded from the drive envelope determination may beincluded and agents previously included may be excluded. Moreover, eachdrive envelope may be based on a propagation of predicted motion, e.g.,of the vehicle and/or of the agent(s). Thus, for example, the positionalong the path at which the lateral distances are determined may includean approximation of where agents will be when the vehicle is at theposition.

Block 316 illustrates determining one or more drive trajectories in thedrive envelope. For example, the planning system 226 can determine oneor more trajectories for navigating the vehicle 202 within the driveenvelope (e.g. by performing path planning (e.g. A*, RRT*, Dijkstra'salgorithm, in addition to/or alternatively to, non-linear least squareoptimizations over a trajectory, etc.), while using the boundaries ofthe drive envelope and/or any encoded information therein asconstraints. For instance, as in the example of FIG. 1, the predictiondata may indicate that an agent in front of the vehicle 202 is slowingdown. In response, the planning system 226 can determine a trajectory inthe drive envelope that causes the vehicle 202 to change lanes inresponse to the slowing vehicle. In this manner, the vehicle 202 canavoid waiting for the vehicle to clear the current lane. Othertrajectories are contemplated herein, and these examples are notintended to be limiting.

Block 318 illustrates executing the trajectory. As described above, thevehicle computing device(s) 204 can include one or more systemcontrollers 230, which can be configured to control steering,propulsion, braking, safety, emitters, communication, and other systemsof the vehicle 202. These system controller(s) 230 can communicate withand/or control corresponding systems of the drive module(s) 214 and/orother components of the vehicle 202. In at least one example, the systemcontroller(s) 228 can receive the trajectory and can communicate withand/or control corresponding systems of the drive module(s) 214 and/orother components of the vehicle 202 such to cause the vehicle 202 tonavigate along the trajectory in the drive envelope.

FIG. 4 is a flowchart illustrating an example method 400 for determininga drive envelope, as described herein. For example, the method 400 maygenerally correspond to block 308 and/or block 314 in FIG. 3, althoughblocks 308 and/or 314 are not limited to the example method 400, and themethod 400 is not limited to inclusion in the method 300.

Block 402 illustrates determining a planned path through theenvironment. For example, scene 404 is provided proximate block 402 toillustrate the environment 100 from FIG. 1 (for clarity, most referencenumerals are omitted from FIG. 4). The scene 404 is provided merely asan example implementation of techniques described herein. Othersituations will vary, depending upon the environment, agents in theenvironment, predicted actions of those agents, or the like. In thescene 404, two vehicles 104 a, 104 b, and two pedestrians 106 a, 106 bare determined to be in the environment. Based on the relative position,relative motion, and/or other information, the planning system 226 candetermine a planned path 406 along which it is predicted that thevehicle 102 can traverse. For example, the planned path 406 may be apath that is offset a minimum distance relative to agents in theenvironment (e.g., the agents determined to be included in the driveenvelope calculation at 308 in method 300), is centered relative tolanes of the road, is offset relative to road markings, is determinedbased on other features, e.g., traffic rules, road signs, or the like.In some examples, the planned path 406 is not a path along which thevehicle is intended to travel, although it may be determined using manyof the same systems that determine an actual drive path through theenvironment, e.g., the planning system 226, the prediction system 224,or the like. Although only the single planned path 406 is illustrated inthe scene 404, the planned path 406 may be selected from a plurality ofplanned paths. For example, multiple planned paths may be determined bythe planning system 226, with the planned path 406 being chosen from themultiple paths. In implementations, the planned path 406 may be chosenbased on its proximity to agents, e.g., a path that travels closer tomore agents may be disfavored relative to a path that travels closer tofewer agents. In other implementations, a shorter path may be favoredover a longer path. Other criteria may also or alternatively be used tomake the final determination.

Block 408 illustrates determining one or more attributes associated witheach of a plurality of objects in the environment. For example, asdescribed herein, the plurality of objects for which attributes areassociated may be the objects determined to be included the driveenvelope, e.g., at block 308 of the method of FIG. 3. The attributes mayinclude and/or overlap with the information used in the method 308 todetermine whether to include the objects in the drive envelopedetermination. For example, current attributes of the agents may includethe agent's semantic classification, pose, position (e.g., in a local orglobal coordinate system and/or relative to the vehicle), extent,heading, velocity, and/or acceleration, in addition to any uncertaintyassociated with any one or more attributes. For example, the attributesmay be determined based on the sensor data about the object and/or usingone or more of the planner system, the prediction system, or the like.

In example implementations, the attributes may include semanticinformation associated with the agent(s). For instance, based on thesensor data, the vehicle computing device(s) 204, e.g., the localizationsystem 220 and/or the perception system 222 may classify each of theobjects, e.g., as a vehicle, a person, a specific animal, a trafficsign, or the like. Moreover, the semantic information may also includefurther sub-classifications. For example, a vehicle may be classified asa moving vehicle or a parked vehicle. The agent information may alsoinclude probabilistic and/or uncertainty information associated with theagent (e.g. with respect to position, velocity, acceleration, semanticclassification, etc). In some implementations, the prediction system 228may determine a predicted likelihood of a course of action by an agent.For example, in the scene 404, the likelihood that the vehicle 104 bmoves from its position may approach zero, for example, if there is nodriver in the vehicle 104 b. Similarly, the likelihood that thepedestrian 106 b continues on a trajectory on the shoulder of the road,substantially parallel to the road, may be determined based on empiricaldata stored as agent information in the agent database 232.

Block 410 illustrates determining, for each of multiple positions on theplanned path, a first distance between the respective position and oneor more objects lateral to that position. Scene 412 is a portion of thescene 404 and illustrates the processing of block 410. Specifically, adistance 414 is shown as a distance between the planned path 406 and thepedestrian 106 a, a distance 416 is shown as a distance between theplanned path and the moving vehicle 104 a, and a distance 418 is shownas a distance between the planned path and the stationary vehicle 104 b.A lateral distance to the pedestrian 106 b is not determined because thepedestrian 106 b is not to be included in the drive envelopecalculation. For example, because of the relative speed of the vehicle102, the pedestrian 106 b, even if making a drastic maneuver such asrunning out into the road would not impact the vehicle 102. The lateraldistances 414, 416, 418 may be perpendicular to a segment of the plannedpath 406 proximate each position. Thus, although not illustrated, thelateral distances along the path 406 when the path is crossing betweenlanes would be oblique relative to the direction of travel. Stateddifferently, each of the lateral distances may be measured along alateral axis of a vehicle at a plurality of discrete poses the vehiclewould have while travelling along the planned path 406. Moreover,consideration of each of the discrete poses may also include apropagation of the agents in the environment. Thus, for example,considering an advanced position 5 meters along the planned path mayalso include a prediction of where each of the agents will be when thevehicle has advanced that 5 meters. In implementations, the lateraldimensions may be determined using image processing techniques,including ray casting.

As part of block 410, the lateral dimensions may be determined for anynumber of positions along the projected path. For example, the lateraldimensions may be determined for discrete points every 10 cm to 100 cmalong the planned path. Information from these discrete points may thenbe interpolated to create the drive envelope. Moreover, although thediscrete points may be evenly spaced, in other implementations thediscretization may be adaptive. For example, for relatively (laterally)closer agents, the frequency of points considered may be increased(e.g., more points per unit distance may be considered). Moreover, thepoints may be determined based on features of the agents. For example,points at a corner, leading edge, and/or trailing edge of an agent maybe used to determine the drive envelope according to implementations ofthis disclosure. As also illustrated, there are portion of the plannedpath 406 at which no agents are laterally disposed. In theseimplementations, the lateral distances may be measured to other physicalobjects, including but not limited to road signs, road markings, curbs,and the like. In some implementations, map data may also be used toaugment the sensor data, e.g., to determine a lateral extent of a roadsurface when no object is detected in that direction. Additionally, oralternatively, such objects may be propagated in time (e.g. according toa probabilistic model) such that future features (positions, velocities,uncertainties, semantic classifications, and correspondinguncertainties) of the objects (agents) are incorporated into the driveenvelope as well.

Block 420 illustrates determining a second distance for each of thepositions, based on the attributes determined at block 408 and the firstdistances determined at block 410. For example, a distance 422, lessthan the distance 414, may be determined for the pedestrian 106 a, adistance 424, less than the distance 416 may be determined for themoving vehicle 104 a, and a distance 426, less than the distance 418,may be determined for the stationary vehicle 104 b. In examples of thisdisclosure, the distances 422, 424, 426 may be based on semanticinformation. For example, different classifications of objects may havedifferent associated safety factors, and minimum offsets may beassociated with those safety factors. By way of non-limiting example,pedestrians may have a first safety factor associated therewith, and aminimum distance the vehicle should maintain may be a relatively largedistance, e.g., on the order of about 0.5 to about 0.8 meters.Additional safety factors may be associated with other agents. Forexample, a minimum distance of 0.1 m or less may be associated with astationary, inanimate object, such as parked car, a construction cone,or road sign. Moreover, within classes of agents, different safetyfactors may be applicable. In other implementations, these “minimum”distances may be preferred distances, with vehicle being able todetermine costs associated with going below those minimum distances.

The distances 422, 424, 426 may also be determined based onprobabilistic information. For example, inasmuch as the lateraldistances 414, 416, 418 are determined using mathematical techniques,there will be some uncertainty in those calculated distances. Moreover,uncertainty associated with the sensors providing the sensor data andwith the prediction models may also be considered when determining thedistances 422, 424, 426. In one example implementation, for example,each of the distances 414, 416, 418 may be determined based on aGaussian distribution. Moreover, the covariance of that distribution mayprovide filtering that allows for further determination of the adjusteddistances 422, 424, 426. In various examples, the lateral distances,semantic classifications, object features, and correspondinguncertainties associated with discrete lengths along the drive envelopemay be encoded into the boundaries for use in trajectory generation andoptimization (e.g. as a cost or constraint for path planning asdescribed in detail above).

Block 428 illustrates determining a drive envelope having lateraldistances equal to the second distances at each position. For example,the second distances 422, 424, 426 determined for each of the positionsare fused together into a single drive envelope 430 in which the vehiclecan travel through the environment. As will be appreciated, the driveenvelope will have varied lateral extents to accommodate for differentagents. As illustrated, the drive envelope may determine a relativelylarger offset for the pedestrian 106 b than for either the movingvehicle 104 a or the stationary vehicle 104 b. Similarly, the lateraloffset relative to the moving vehicle 104 a may be larger than for thestationary vehicle 104 b. For points along the projected path for whichthere is no laterally-disposed agent, the second distance may becalculated as a standard offset, e.g., 0.1 meter, from a lane markingand/or some other static feature in the environment.

The various techniques described herein can be implemented in thecontext of computer-executable instructions or software, such as programmodules, that are stored in computer-readable storage and executed bythe processor(s) of one or more computers or other devices such as thoseillustrated in the figures. Generally, program modules include routines,programs, objects, components, data structures, etc., and defineoperating logic for performing particular tasks or implement particularabstract data types.

Other architectures can be used to implement the describedfunctionality, and are intended to be within the scope of thisdisclosure. Furthermore, although specific distributions ofresponsibilities are defined above for purposes of discussion, thevarious functions and responsibilities might be distributed and dividedin different ways, depending on circumstances.

Similarly, software can be stored and distributed in various ways andusing different means, and the particular software storage and executionconfigurations described above can be varied in many different ways.Thus, software implementing the techniques described above can bedistributed on various types of computer-readable media, not limited tothe forms of memory that are specifically described.

Example Clauses

A: An example vehicle includes: one or more sensors disposed on thevehicle; one or more processors; and memory storing processor-executableinstructions that, when executed by the one or more processors,configure the vehicle to: receive sensor data from a sensor of the oneor more sensors, the sensor data comprising information about an objectin an environment proximate the vehicle; determine a planned path of thevehicle through the environment; determine a lateral distance betweenthe planned path and the object; determine a classification of theobject; determine, based at least in part on the lateral distance andthe classification, a width; and determine a drive envelope, wherein thedrive envelope defines a boundary of a portion of the environment alongthe planned path in which the vehicle can travel and wherein at least aportion of the envelope extends the width laterally from the plannedpath and toward the object.

B: The vehicle of example A, wherein the classification comprises astatic object or a lane boundary and wherein the instructions, whenexecuted by the one or more processors, further configure the vehicleto: determine, based on the classification, a minimum distance tomaintain between the vehicle and the object, and a difference betweenthe lateral distance and the width is based at least in part on theminimum distance.

C: The vehicle of example A or example B, wherein the classificationcomprises a car, a bicycle, a motorcycle or a pedestrian, and whereinthe instructions, when executed by the one or more processors, furtherconfigure the vehicle to determine an uncertainty associated withmovement of the object or the sensor, wherein the width is based atleast in part on the uncertainty.

D: The vehicle of any one of example A through example C, wherein theinstructions, when executed by the one or more processors, furtherconfigure the vehicle to: determine, based at least in part on the driveenvelope, a trajectory, the trajectory defining motion of the vehicle inthe environment and within the drive envelope; and control the vehicleto travel through the environment according to the trajectory.

E: An example method includes: determining a planned path of a vehiclethrough an environment, the planned path beginning at an origin andending at a destination; detecting, based at least in part on sensordata acquired from a sensor on the vehicle, an object in theenvironment; determining a plurality of points along the planned path;determining lateral distances from each of the plurality of points tothe object; determining a classification of the object; determining,based at least in part on the lateral distances and the classification,one or more widths; and determining, based at least in part on the oneor more widths, a drive envelope through the environment, the driveenvelope comprising a portion of the environment along the planned pathin which the vehicle can travel.

F: The method of example E, further comprising: determining, based onthe classification, a minimum distance to maintain between the vehicleand the object, wherein determining the one or more widths comprises:determining that differences between the lateral distances and theminimum distance do not meet or exceed a lane width; and determining, asthe one or more widths, the differences.

G: The method of example E or example F, wherein the classificationcomprises a car, a bicycle, a motorcycle or a pedestrian, the methodfurther comprising: determining a plurality of uncertainties associatedwith a movement of the object in the environment or the sensor; andassociating the plurality of uncertainties with the plurality of points;wherein the plurality of widths is based at least in part on theplurality of uncertainties.

H: The method of any one of example E through example G, wherein theplurality of uncertainties is based at least in part on a covariance ofa probability function associated with the object.

I: The method of any one of example E through example H, furthercomprising: determining a predicted movement of the object; anddetermining a probability associated with the predicted movement,wherein the determining the drive envelope is based at least in part onthe probability associated with the predicted movement.

J: The method of any one of example E through example I, wherein theplurality of points are associated with the classification, a pluralityof distances to the object, or a plurality of uncertainties.

K: The method of any one of example E through example J, wherein theplurality of uncertainties are associated with movement of the object,and wherein determining the plurality of uncertainties comprisespropagating a probabilistic model of the object in time.

L: The method of any one of example E through example K, furthercomprising: determining, in accordance with a receding horizon techniqueand based at least in part drive envelope, a plurality of trajectories,the plurality of trajectories associated with a plurality of confidencelevels; selecting, as a preferred trajectory, a trajectory of theplurality of trajectories associated with a highest confidence level;and controlling the vehicle to travel through the environment along thepreferred trajectory and within the drive envelope.

M: The method of any one of example E through example L, whereindetermining the plurality of trajectories comprises a non-linear leastsquares optimization or differential dynamic programming using theinformation encoded in the drive envelope as a cost.

N: An example non-transitory computer-readable medium having a set ofinstructions that, when executed, cause one or more processors toperform operations comprising: receiving sensor data from a sensor, thesensor data comprising information about an object in an environment ofthe vehicle; determining a planned path of the vehicle through theenvironment, the planned path comprising a path for the vehicle totraverse from an origin to a destination; determining, based at least inpart on the sensor data, a distance between a first position on theplanned path and the object, the distance being measured in a directionperpendicular to the planned path at the first position; determining aclassification of the object; determining, based at least in part on thedistance and the classification, a width; and determining a driveenvelope in the environment, the drive envelope comprising a surface ofthe environment along the planned path on which the vehicle can travelwithout colliding with the object, a portion of the envelope extendingthe width laterally from the planned path at the first position andtoward the first object.

O: The non-transitory computer-readable medium of example N, theoperations further comprising: determining an uncertainty associatedwith a position of the object, a velocity of the object, or anacceleration of the object; determining, based at least on the width andthe uncertainty, a trajectory; and controlling the vehicle to travelthrough the environment along the trajectory.

P: The non-transitory computer-readable medium of example N or exampleO, wherein the planned path is a first planned path and the driveenvelope is a first drive envelope, the operations further comprising:determining a second planned path different from the first planned path;determining a second drive envelope comprising a second surface of theenvironment along the second planned path on which the vehicle cantravel without colliding with the object; selecting one of the firstdrive envelope or the second drive envelope as the preferred driveenvelope; determining, based at least in part on the preferred driveenvelope, a trajectory, the trajectory defining a discrete motion of thevehicle in the environment and within the preferred drive envelope; andcontrolling the vehicle to travel through the environment along thetrajectory.

Q: The non-transitory computer-readable medium of any one of example Nthrough example P, wherein the classification comprises a car, apedestrian, or a bicycle, the method further comprising: determining,based on the classification, a safety margin.

R: The non-transitory computer-readable medium of any one of example Nthrough example Q, the operations further comprising: determining adifference between the distance and the safety margin; and determiningthat the difference does not meet or exceed a lane width associated withthe first position; and determining, as the width, the difference.

S: The non-transitory computer-readable medium of any one of example Nthrough example R, the operations further comprising: determininginformation associated with movement of the object, the informationcomprising predicted future locations of the object and uncertaintiesassociated with the predicted future locations, wherein determining thewidth is based at least in part on the predicted future locations of theobject and the uncertainties.

T: The non-transitory computer-readable medium of any one of example Nthrough example S, wherein determining the drive envelope comprisesdetermining, for additional positions along the planned path, additionalwidths for the drive envelope, the additional widths being based atleast in part on a movement of the object or a probability associatedwith the movement.

CONCLUSION

While one or more examples of the techniques described herein have beendescribed, various alterations, additions, permutations and equivalentsthereof are included within the scope of the techniques describedherein.

In the description of examples, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific examples of the claimed subject matter. It is to be understoodthat other examples can be used and that changes or alterations, such asstructural changes, can be made. Such examples, changes or alterationsare not necessarily departures from the scope with respect to theintended claimed subject matter. While the steps herein can be presentedin a certain order, in some cases the ordering can be changed so thatcertain inputs are provided at different times or in a different orderwithout changing the function of the systems and methods described. Thedisclosed procedures could also be executed in different orders.Additionally, various computations described herein need not beperformed in the order disclosed, and other examples using alternativeorderings of the computations could be readily implemented. In additionto being reordered, in some instances, the computations could also bedecomposed into sub-computations with the same results.

What is claimed is:
 1. A vehicle comprising: one or more sensorsdisposed on the vehicle; one or more processors; and memory storingprocessor-executable instructions that, when executed by the one or moreprocessors, configure the vehicle to: receive sensor data from a sensorof the one or more sensors, the sensor data comprising information aboutan object in an environment proximate the vehicle; determine a plannedpath of the vehicle through the environment; determine a lateraldistance between the planned path and the object; determine aclassification of the object; determine, based at least in part on thelateral distance and the classification, a width; and determine a driveenvelope, wherein the drive envelope defines a boundary of a portion ofthe environment along the planned path in which the vehicle can traveland wherein at least a portion of the envelope extends the widthlaterally from the planned path and toward the object.
 2. The vehicle ofclaim 1, wherein the classification comprises a static object or a laneboundary and wherein the instructions, when executed by the one or moreprocessors, further configure the vehicle to: determine, based on theclassification, a minimum distance to maintain between the vehicle andthe object, and a difference between the lateral distance and the widthis based at least in part on the minimum distance.
 3. The vehicle ofclaim 1, wherein the classification comprises a car, a bicycle, amotorcycle or a pedestrian, and wherein the instructions, when executedby the one or more processors, further configure the vehicle todetermine an uncertainty associated with movement of the object or thesensor, wherein the width is based at least in part on the uncertainty.4. The vehicle of claim 1, wherein the instructions, when executed bythe one or more processors, further configure the vehicle to: determine,based at least in part on the drive envelope, a trajectory, thetrajectory defining motion of the vehicle in the environment and withinthe drive envelope; and control the vehicle to travel through theenvironment according to the trajectory.
 5. A method comprising:determining a planned path of a vehicle through an environment, theplanned path beginning at an origin and ending at a destination;detecting, based at least in part on sensor data acquired from a sensoron the vehicle, an object in the environment; determining a plurality ofpoints along the planned path; determining lateral distances from eachof the plurality of points to the object; determining a classificationof the object; determining, based at least in part on the lateraldistances and the classification, one or more widths; and determining,based at least in part on the one or more widths, a drive envelopethrough the environment, the drive envelope comprising a portion of theenvironment along the planned path in which the vehicle can travel. 6.The method of claim 5, further comprising: determining, based on theclassification, a minimum distance to maintain between the vehicle andthe object, wherein determining the one or more widths comprises:determining that differences between the lateral distances and theminimum distance do not meet or exceed a lane width; and determining, asthe one or more widths, the differences.
 7. The method of claim 5,wherein the classification comprises a car, a bicycle, a motorcycle or apedestrian, the method further comprising: determining a plurality ofuncertainties associated with a movement of the object in theenvironment or the sensor; and associating the plurality ofuncertainties with the plurality of points; wherein the plurality ofwidths is based at least in part on the plurality of uncertainties. 8.The method of claim 7, wherein the plurality of uncertainties areassociated with movement of the object, and wherein determining theplurality of uncertainties comprises propagating a probabilistic modelof the object in time.
 9. The method of claim 7, wherein the pluralityof uncertainties is based at least in part on a covariance of aprobability function associated with the object.
 10. The method of claim5, further comprising: determining a predicted movement of the object;and determining a probability associated with the predicted movement,wherein the determining the drive envelope is based at least in part onthe probability associated with the predicted movement.
 11. The methodof claim 5, wherein the plurality of points are associated with theclassification, a plurality of distances to the object, or a pluralityof uncertainties.
 12. The method of claim 11, further comprising:determining, in accordance with a receding horizon technique and basedat least in part drive envelope, a plurality of trajectories, theplurality of trajectories associated with a plurality of confidencelevels; selecting, as a preferred trajectory, a trajectory of theplurality of trajectories associated with a highest confidence level;and controlling the vehicle to travel through the environment along thepreferred trajectory and within the drive envelope.
 13. The method ofclaim 12, wherein determining the plurality of trajectories comprises anon-linear least squares optimization or differential dynamicprogramming using the information encoded in the drive envelope as acost.
 14. A non-transitory computer-readable medium having a set ofinstructions that, when executed, cause one or more processors toperform operations comprising: receiving sensor data from a sensor, thesensor data comprising information about an object in an environment ofthe vehicle; determining a planned path of the vehicle through theenvironment, the planned path comprising a path for the vehicle totraverse from an origin to a destination; determining, based at least inpart on the sensor data, a distance between a first position on theplanned path and the object, the distance being measured in a directionperpendicular to the planned path at the first position; determining aclassification of the object; determining, based at least in part on thedistance and the classification, a width; and determining a driveenvelope in the environment, the drive envelope comprising a surface ofthe environment along the planned path on which the vehicle can travelwithout colliding with the object, a portion of the envelope extendingthe width laterally from the planned path at the first position andtoward the first object.
 15. The non-transitory computer-readable mediumof claim 14, the operations further comprising: determining anuncertainty associated with a position of the object, a velocity of theobject, or an acceleration of the object; determining, based at least onthe width and the uncertainty, a trajectory; and controlling the vehicleto travel through the environment along the trajectory.
 16. Thenon-transitory computer-readable medium of claim 14, wherein the plannedpath is a first planned path and the drive envelope is a first driveenvelope, the operations further comprising: determining a secondplanned path different from the first planned path; determining a seconddrive envelope comprising a second surface of the environment along thesecond planned path on which the vehicle can travel without collidingwith the object; selecting one of the first drive envelope or the seconddrive envelope as the preferred drive envelope; determining, based atleast in part on the preferred drive envelope, a trajectory, thetrajectory defining a discrete motion of the vehicle in the environmentand within the preferred drive envelope; and controlling the vehicle totravel through the environment along the trajectory.
 17. Thenon-transitory computer-readable medium of claim 14, wherein theclassification comprises a car, a pedestrian, or a bicycle, the methodfurther comprising: determining, based on the classification, a safetymargin.
 18. The non-transitory computer-readable medium of claim 17, theoperations further comprising: determining a difference between thedistance and the safety margin; and determining that the difference doesnot meet or exceed a lane width associated with the first position; anddetermining, as the width, the difference.
 19. The non-transitorycomputer-readable medium of claim 14, the operations further comprising:determining information associated with movement of the object, theinformation comprising predicted future locations of the object anduncertainties associated with the predicted future locations, whereindetermining the width is based at least in part on the predicted futurelocations of the object and the uncertainties.
 20. The non-transitorycomputer-readable medium of claim 14, wherein determining the driveenvelope comprises determining, for additional positions along theplanned path, additional widths for the drive envelope, the additionalwidths being based at least in part on a movement of the object or aprobability associated with the movement.